يعرض 1 - 20 نتائج من 194 نتيجة بحث عن '(((( image using algorithms ) OR ( image modeling algorithm ))) OR ( element method algorithm ))', وقت الاستعلام: 0.15s تنقيح النتائج
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    A machine learning model for early detection of diabetic foot using thermogram images حسب Amith Khandakar (14151981)

    منشور في 2021
    "…A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. …"
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    Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study حسب Alkhodari, Mohanad Ahmed

    منشور في 2021
    "…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …"
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    article
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    Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach حسب Arshad Ali Khan (23152516)

    منشور في 2025
    "…This paper proposes a new approach using convolutional neural networks with genetic algorithms for estimating air quality directly from images. …"
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    A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net حسب Sania Gul (18272227)

    منشور في 2023
    "…Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. …"
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    Digital Image Watermarking Using Balanced Multiwavelets حسب Ghouti, L.

    منشور في 2006
    "…The embedding scheme is image adaptive using a modified version of a well-established perceptual model. …"
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    article
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    Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models حسب Theng, Lau Wei

    منشور في 2022
    "…This paper presents an image classification method on 4 types of salak (salak pondoh, salak gading, salak sideempuan and salak affinis) using a Convolutional Neural Network (CNN), VGG16 and ResNet50. …"
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    Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array حسب Amran Hossain (570706)

    منشور في 2022
    "…Later, a nine-antenna array-based microwave brain imaging (MBI) system is implemented and investigated by using phantom model. …"
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    Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier حسب JAIN, SWETA

    منشور في 2023
    "…The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. …"
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    Nonlinear analysis of shell structures using image processing and machine learning حسب M.S. Nashed (16392961)

    منشور في 2023
    "…The proposed method in this paper is unique in that it converts the mechanical behaviour of shell structures into images that are then used to train a machine learning algorithm. …"
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    Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine حسب Debendra Muduli (20748758)

    منشور في 2024
    "…<p dir="ltr">Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. …"
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    Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images حسب Rehan Raza (17019105)

    منشور في 2023
    "…Moreover, it is faster and requires fewer parameters to train than other CNN based models, making it a good choice for large-scale deployment in clinical settings and a promising tool for automated lung cancer diagnosis from CT scan images.…"